Artificial Intelligence
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Artificial Intelligence (@artificial_intelligence_com) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 72 399 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 1 724-o'rinni va Hindiston mintaqasida 4 344-o'rinni egallagan.
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невідомо sanasidan buyon loyiha tez o‘sib, 72 399 obunachiga ega bo‘ldi.
31 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 363 ga, so‘nggi 24 soatda esa -19 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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Yuqori yangilanish chastotasi (oxirgi ma’lumot 01 Sentabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Postlar arxiv
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📱Artificial Intelligence and Machine Learning
📱High-Performance PySpark: Advanced Strategies for Optimal Data Processing
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📂 Full description
Master the art of efficient data processing with this advanced PySpark course designed for data engineers. Instructor Ameena Ansari shows you the essentials of optimizing the data cleaning process and defining schemas to streamline ingestion at scale. Explore various data formats and compression techniques to ensure seamless performance, even with massive datasets. By the end of this course, you'll have the tools and skills you need to transform and ingest high-quality data using PySpark pipelines that are both scalable and efficient.This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that youll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.
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🔅 High-Performance PySpark: Advanced Strategies for Optimal Data Processing
🌐 Author: Ameena Ansari
🔰 Level: Advanced
⏰ Duration: 1h 22m
🌀 Discover techniques for optimizing data cleaning, selecting efficient data formats, minimizing shuffling and skew, and performing high-performance data processing at scale.📗 Topics: Data Pipelines, PySpark, Data Processing 📤 Join Artificial Intelligence and Machine Learning for more courses
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🔗 30 Useful AI Apps That Can Help You in 2025
AI apps are taking over the world. There’s an AI app for every conceivable use case. Here are some AI apps for different categories:1 - General Purpose: Perplexity, Anthropic Claude, Grok, ChatGPT, and Gemini 2 - Writing Code: Cursor, Replit, Windsurf AI, Github Copilot, and Tabnine 3 - Productivity: Adobe (PDF Chat), Gemini for Gmail, Gamma (AI slide deck), WisprFlow (AI voice dictation), and Granola (AI notetaker) 4 - Audience Building: Delphi (AI text, voice), HeyGen (video translation), Persona (AI agent builder), Captions (AI video editing), and OpusClips (Video repurposing) 5 - Creativity: ElevenLabs (realistic AI voices), Midjourney, Suno AI (music generation), Krea (enhance images), and Photoroom (AI image editing) 6 - Learning and Growth: Particle News App, Rosebud (AI journal app), NotebookLM, GoodInside (parenting co-pilot), and Ash (AI counselor).
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📱Artificial Intelligence and Machine Learning
📱Artificial Intelligence Foundations: Machine Learning
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📂 Full description
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If youre looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
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🔅 Artificial Intelligence Foundations: Machine Learning
🌐 Author: Kesha Williams
🔰 Level: Beginner
⏰ Duration: 1h 50m
🌀 Learn about the machine learning lifecycle and the steps required to build systems in this hands-on course.📗 Topics: Machine Learning, Artificial Intelligence 📤 Join Artificial Intelligence and Machine Learning for more courses
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8. Set up the user interface and trigger the main function.
• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"
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7. Define the main function to run all LLMs and aggregate results.
• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.
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6. Implement the LLM call function.
• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response
